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第41届中国控制会议

作者： Mingxiang Liao Yongbo Li Guangyao Wang Zhenyu Lei Zihao Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Ultra-wideband（UWB） positioning technology has absolute advantages due to its centimeter-level accurate positioning ***,the positioning accuracy is greatly reduced because of the inability to completely solve the error caused by non-line-of-sight（NLOS） and multi-path *** present,the Chan-Taylor（C-T） combination algorithm proposed by scholars can achieve a good positioning effect in the line-of-sight（LOS） environment,but in the NLOS environment,positioning deviation of this algorithm is large .Aiming at the problem of low localization accuracy in NLOS environment,this paper presents a Chan-Taylor-Kalman（CTK） joint positioning algorithm based on UWB and adapted to NLOS *** basic idea of CTK joint positioning algorithm is to introduce weight coefficients on the basis of C-T combination algorithm to reduce the situation of non-convergence of positioning results;then combined with the improved Kalman filter to eliminate outliers and eliminate the influence of the non-line-of-sight *** show that under the same NLOS environment,the probability of the positioning error of the C-T combination positioning algorithm greater than 20 cm is about 60%,while the probability of the positioning error of the CTK joint positioning algorithm greater than 10 cm is only about 10%.

关键词： UWB NLOS indoor positioning Chan-Taylor combination algorithm Kalman filter

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A NOVEL ARTIFICIAL POTENTIAL FIELD APPROACH CONSIDERING ANGULAR ACCELERATION CONSTRAINT

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International Journal of Robotics and Automation 2024年第1期39卷 24-30页

作者： Sun, Mingxiao Yuan, Zhangjie Zhang, Yongde Zhen, Liqiang Luan, Tiantian Yuan, Xiaoliang Li, Xiaogang School of Automation Harbin University of Science and Technology No. 52 Xuefu Road Nangang District Heilongjiang Province Harbin City150080 China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and Technology No. 52 Xuefu Road Nangang District Heilongjiang Province Harbin City150080 China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology No. 52 Xuefu Road Nangang District Heilongjiang Province Harbin City150080 China Wuxi CREG Urban Rail Transit Equipment Co. Ltd. No. 1061 Jiaoshan Road Xishan Development Zone Jiangsu Province Wuxi City214000 China

An improved artificial potential field approach (APFA) is proposed for unmanned surface vehicle (USV) in path planning. The proposed approach can effectively avoid easily falling into local minima, also, plan the optimal path. Firstly, a map of the Marine environment is established, and the obstacle is treated with circular expansion. Especially, the expansion coefficient is introduced to ensure the safety of navigation. Secondly, aiming at the local minimum problem, a rotation angle formula satisfying its own maximum rotation angle and angular acceleration constraints is constructed, so that a path to avoid local minimum is planned. Moreover, the local minimum of the complex environment is considered, the virtual repulsive force potential field is constructed to generate virtual repulsive force on USV to avoid local minimum. Numerical simulation results show that the proposed approach can complete path planning under different complex and special environments. © 2024 Acta Press. All rights reserved.

关键词： Motion planning

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A Weighting Factors Autotuning Strategy for Model Predictive Torque Control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm 42

A Weighting Factors Autotuning Strategy for Model Predictive...

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42nd Chinese Control Conference, CCC 2023

作者： Zhou, Zixiang Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Cost functions

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network 42

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, YiFei Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Finite element method

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Target extraction based on hybrid domain features of array GM-APD lidar 10

Target extraction based on hybrid domain features of array G...

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10th Symposium on Novel Optoelectronic Detection Technology and Applications

作者： Zhang, Xin Li, Sining Sun, Jianfeng Jiang, Peng Jin, Haowei National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Henan Zhengzhou450000 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

This study proposes a target extraction algorithm for array GM-APD lidar based on hybrid domain features, designed to address the challenges of accurately extracting target region based on the image in complex scenes and high noise interference. We extracted the temporal features of the echo signals and transformed the echo signals into the image domain to enhance the information content and further extract image features. By integrating multi-view K-means clustering and AdaBoost classification methods, we introduced a dynamic weight adjustment mechanism and a region adjustment method based on a correlation coefficient to achieve pixel-type discrimination and target region extraction. Experimental results demonstrate that the proposed method exhibits good robustness and adaptability in complex detection environments. Under conditions of different occlusion, different signal-to-noise ratios, and different target reflectivity, the proposed algorithm significantly outperforms traditional region-growing segmentation methods in terms of accuracy of target region extraction. © 2025 SPIE.

关键词： K-means clustering

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Human-Robot Collaborative Decision Method of Hexapod Robot Based on Prior Knowledge and Negotiation Strategy

SSRN

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SSRN 2024年

作者： You, Bo Chen, Xiaolei Li, Jiayu Ding, Liang Dong, Zheng The Key Laboratory of Intelligent Technology for Cutting and Manufacturing Ministry of Education Harbin University of Science and Technology Harbin150080 China The Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin University of Science and Technology Harbin150080 China The Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form robot’s decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot’s decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Simulation study on control characteristics of semiconductor grade monocrystalline silicon growth process

Simulation study on control characteristics of semiconductor...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Yuyu Liu, Ding Song, Zezhong Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi`an University of Technology Xi'an710048 China

ISBN: (纸本)9781665465335

The growth of Czochralski(CZ) monocrystalline silicon is a nonlinear dynamic time-varying system with complex physical changes and multi-field and multi-phase coupling. In order to meet the needs of information development, it is urgent to develop large-scale, high-quality monocrystalline silicon. However, it is extremely difficult to establish an accurate control system model. Therefore, this paper considers the complex physical changes of multi-field coupling and non-linear processes in the growth of CZ monocrystalline silicon, a lumped parameter model for the growth of monocrystalline silicon is established based on energy balance model, fluid mechanics model, and geometry model, according to the internal mechanism of the monocrystalline silicon growth and the transfer mechanism of internal material flow. According to the model, a CZ monocrystalline silicon growth simulation control system is established under the Matlab/Simulink environment, and the real-time simulation data of each state variable of crystal growth is obtained. So that the control characteristics of each state variable in the crystal growth process can be analyzed. Finally, the validity of the established model is checked by the records evaluation of the semiconductor industry production process. Finally, the validity of the established model is verified based on the data analysis of the semiconductor industry production process. The results show that the model has high accuracy and can better describe the dynamic behavior of the actual monocrystalline silicon growth system. © 2022 IEEE.

关键词： MATLAB

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Model predictive control of event triggering in silicon single crystal growth processes

Model predictive control of event triggering in silicon sing...

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Yankai Ren, Biao Liu, Ding Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an710048 China

ISBN: (纸本)9781665465335

In this paper, the control issue of the semiconductor silicon monocrystalline growth process is studied through an event triggered model prediction approach. Firstly, a state-space model of silicon monocrystalline growth process is developed to describe the energy conversion and the dynamics and the geometry of the isodiametric stage silicon monocrystalline via the Czochralski method. Secondly, a small perturbation linearization is used to obtain a linear continuous model, which is translated into a linear discrete model under zero-order retainers. Finally, an event triggered model prediction controller is established to decrease the calculation of model prediction in the rolling optimization process. Based on experimental results, the event-triggered model prediction control algorithm is confirmed to be an effectively control scheme for the silicon monocrystalline growth process. © 2022 IEEE.

关键词： Model predictive control

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Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning

Experimental research on GM-APD LIDAR point cloud classifica...

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2022 Applied Optics and Photonics China: Advanced Laser Technology and Applications, AOPC 2022

作者： Jiang, Yanze Sun, Jianfeng Ding, Yuanxue Jiang, Peng Zhang, Hailong Li, Sining Zhou, Shuaijun National Key Laboratory of Science and Technology on Tunable Laser Institute of OptoElectronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510662223

Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm-APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification *** that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research. © 2023 SPIE.

关键词： Optical radar

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